Background There is a need for diagnostic tests of early PD diagnosis. A subset of AI known as deep learning (DL) has shown great promise in diagnostic medical imaging, sometimes outperforming radiolo- gists by detecting patterns invisible to the human eye. Using DL, we explored whether such changes are detectable on routine PD MRI scans.

Methods We trained a convolutional neural network to classify 138 PD and 60 control brain MRI images acquired from the Parkinson’s Progression Marker Initiative (PPMI) database. Models were assessed using k-fold cross-validation. We used Deep SHapley Additive exPlanations (DeepSHAP) to visualise the contri- bution of individual pixels to the model’s prediction.

Results A combined dataset of axial T2 and proton density MRI images was classified with 79% accuracy and an area under the curve (AUC) of 0.86. Respectively T2 and proton density models classified cases with 81/84% accuracy and AUC of 0.83/0.88. DeepSHAP heat maps demonstrated predominant interest in midbrain slices.

Conclusion Our models exhibited good diagnostic performance and demonstrated interest in PD relevant brain regions. We will validate this model in a large dataset of routinely collected NHS MRI scans, many of which predate onset of motor symptoms.